Biological models: Measuring variability with classical and quantum information

Evidence of genomic information and structural restrictions of HIV-1 PR and RT gene regions from individuals experiencing antiretroviral virologic failure

OBJECTIVES

This study analyzed Protease-PR and Reverse Transcriptase-RT HIV-1 genomic information entropy metrics among patients under antiretroviral virologic failure, according to the numbers of virologic failures or resistance mutations.

METHODS

For this purpose, we used genomic sequences from PR and RT of HIV-1 from a cohort of chronic patients followed up at São Paulo Hospital.

RESULTS

Informational entropy proportionally increases with the number of antiretroviral virologic failures in PR and RT (p < .001). Affected regions of PR were related to catalytic and structural functions, such as Fulcrum (K20) Flap (M46) and Cantilever (A71). In RT, this occurred at Fingers (E44) and Palm (K219). Informational entropy increases according to the number of resistance mutations in PR and RT (p < .001). Higher PR entropy was proportional to the resistance mutation numbers in Fulcrum (L10), Active site (L24) Flap (M46), Cantilever (L63) and near Interface (L90). In RT, they related to regions responsible for protein stability such as Fingers (T39) and Palm (L100).

CONCLUSIONS

The antiretroviral selective pressure affects HIV genomic informational entropy at the PR and RT regions, leading to the emergence of more unstable virions. Mapping the three-dimensional structure in these HIV-1 proteins is relevant to designing new antiretroviral targeting resistant strains.

Bits and q-bits in a psychiatric ward

The present study is a trial on expressing the whole state of a psychiatric ward as a linear combination of base states in a Hilbert space. Real data were collected by observing the behavior of the patients from the psychiatric ward of the Clinical Hospital from Faculdade de Medicina de Ribeirão Preto for 12 days and, according to standard procedures, 18 behavioral parameters were daily measured for each patient. The whole data set was analyzed and, by taking the standard grades as eigenstates, the state of the ward was daily expressed by a linear combination of them, allowing the estimation of state transition matrices and of the quantum variability measure. Coefficients of the linear combination can be interpreted as square roots of probabilities and informational entropy is associated to each state resulting in the classical variability measure. Temporal evolutions of the classical and quantum variability measures are plotted trying to relate them to the behavioral state of the whole ward.